8 citations · 20 across the 23 of their papers we have counts for
6 papers · 1 filter
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Haibin Wu, Xuanjun Chen, Yi-Cheng Lin +13
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The i…
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks
Kai-Wei Chang, Haibin Wu, Yu-Kai Wang +6
Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal…
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Haibin Wu, Huang-Cheng Chou, Kai-Wei Chang +5
Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems. However, 80.77% of SER papers yield results that cannot be reproduced. We develop E…
Towards audio language modeling -- an overview
Haibin Wu, Xuanjun Chen, Yi-Cheng Lin +4
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as su…
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin +7
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developme…